Structural Equation Modeling || Information Age Publishing

Structural Equation Modeling

A Second Course

Edited by:
Gregory R. Hancock, University of Maryland
Ralph O. Mueller, The George Washington University

A volume in the series: Quantitative Methods in Education and the Behavioral Sciences: Issues, Research, and Teaching. Series Editor(s): Ronald C Serlin, University of Wisconsin - Madison

Published 2006

This volume is intended to serve as a didactically-oriented resource covering a broad range of advanced topics often not discussed in introductory courses on structural equation modeling (SEM). Such topics are important in furthering the understanding of foundations and assumptions underlying SEM as well as in exploring SEM as a potential tool to address new types of research questions that might not have arisen during a first course. Chapters focus on the clear explanation and application of topics, rather than on analytical derivations, and contain syntax and partial output files from popular SEM software.

REVIEWS
"I believe that this volume represents a vital contribution to the field of SEM beyond the introductory level." - From the Preface by Richard G. Lomax, University of Alabama

"...an important resource for methodologists, applied researchers and students of structural equation modeling(SEM) alike. This well-written editedvolumeprovides coverage of a number of important issues and techniques not commonly treated in a didactic manner and specifically not covered in most introductory SEM textbooks." - Rachel Tanya Fouladi, Simon Fraser University

"We highly recommend this book to academics who are teaching second courses in SEM, to advanced graduate students who seek to expand their understanding of this important class of analytical techniques, and to applied researchers who must apply SEM in their own work. It will definitely be money well spent." ~ Reviewed by Zachary N. J. Horn and J. Matthew Beaubien in Personnel Psychology.

TABLE OF CONTENTS
Introduction to Series, Ronald C. Serlin. Preface, Richard G. Lomax. Dedication. Acknowledgements. Introduction, Gregory R. Hancock & Ralph O. Mueller. Part I: Foundations. The Problem of Equivalent Structural Models, Scott L. Hershberger. Formative Measurement and Feedback Loops, Rex B. Kline. Power Analysis in Covariance Structure Modeling, Gregory R. Hancock. Part II: Extensions. Evaluating Between-Group Differences in Latent Variable Means, Marilyn S. Thompson & Samuel B. Green. Using Latent Growth Models to Evaluate Longitudinal Change, Gregory R. Hancock & Frank R. Lawrence. Mean and Covariance Structure Mixture Models, Phill Gagné. Structural Equation Models of Latent Interaction and Quadratic Effects, Herbert W. Marsh, Zhonglin Wen, & Kit-Tai Hau. Part III: Assumptions. Nonnormal and Categorical Data in Structural Equation Modeling, Sara J. Finney & Christine DiStefano. Analyzing Structural Equation Models with Missing Data, Craig K. Enders. Using Multilevel Structural Equation Modeling Techniques with Complex Sample Data, Laura M. Stapleton. The Use of Monte Carlo Studies in Structural Equation Modeling Research, Deborah L. Bandalos. About the Authors.

RELATED CATEGORIES
> Measurement Research Methods

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